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| 研究生： |
李少鈞 Shao-Chun Lee |
|---|---|
| 論文名稱： |
以六軸機械手臂執行之相機自動校正與物件影像定位系統 Automatic camera calibration and image-based object positioning systems based on 6-axis robot arm |
| 指導教授： |
林其禹
Chyi-Yen Lin |
| 口試委員: |
范欽雄
Chin-Shyurng Fahn 邱士軒 Shih-Hsuan Chiu |
| 學位類別： |
碩士 Master |
| 系所名稱： |
工程學院 - 機械工程系 Department of Mechanical Engineering |
| 論文出版年： | 2018 |
| 畢業學年度： | 106 |
| 語文別： | 中文 |
| 論文頁數： | 88 |
| 中文關鍵詞： | 相機內部參數校正 、鏡頭形變校正 、手眼校正 、影像PNP問題 、物件定位 、工作點修正 |
| 外文關鍵詞： | camera calibration, intrinsic parameter calibration, lens distortion calibration, hand-eye calibration, object positioning, working point adjustment |
| 相關次數： | 點閱：380 下載：6 |
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本研究針對視覺系統在自動化加工中的應用情境提出兩個系統以解決現有的問題。首先針對傳統使用人工進行操作的相機校正流程，提出一個使用機械手臂自動進行校正的解決方案，透過將待校正相機裝設在手臂上以手臂帶動改變相機位置的方式，系統會自動依照校正板所在位置進行路徑規劃，有效率、有系統的引導相機到不同的拍攝點透過不同角度拍攝校正用影像，並且在拍攝校正用影像的同時記錄手臂姿態以進行手眼校正。透過實驗證實本系統能有效的省去人工針對校正用影像取像位置進行篩選的流程，且系統以大量且有效的校正用影像進行相機內部參數與鏡頭形變參數校正並獲得優於人工校正的結果，其內部參數校正誤差為0.112像素。更同時在單一執行過程中自動的完成手眼校正，達到短時間、全自動與三合一的校正成果。其二針對自動化加工中所牽涉到有關物件定位的問題，本研究基於影像系統提出一個新的解決方案。透過在目標物件上貼附人工標記的方式，系統將針對該人工標記拍攝影像，根據物件移動前後的標記位置影像進行數據分析，在不移動物件的前提下即可對目標進行重新定位與修正工作點，使加工過程可以繼續進行。在實驗結果中顯示，系統對於物件的位移量有相當高程度的修正，最高可修正95.3%的位移量，並有相當優秀的定位速度可在500ms內完成定位與工作點修正。本研究提出的兩個系統皆各自以嶄新的概念針對自動化加工流程中常見的問題提出解決方案，並由實驗結果證實能有效的解決及改善問題，且在未來仍有相當大的應用空間與發展潛力，並具有能夠導入實際產業的高度可能性。
This research provides two solutions to solve two exist problems in the automated production process. Firstly, we provide a solution to improve the efficiency and accuracy of a manual camera calibration process by using a robot-arm operated automatic calibration procedure. With the robot arm holding the camera in a predefined positions and orientation to take the pictures on the calibration board and calibrate the camera intrinsic parameters, the lens distortion parameters and hand-eye transform matrix at the same time in a fully autonomous manner. Secondly, in order to tackle the difficulty resulted from poor object positioning situations in the automated production process, we develop a new solution by attaching some artificial landmarks on the target object and using the camera system to perform the calibration based on the image containing the landmarks. In the case that the object has some position errors, the system can compute the compensation displacement needed by the robot arm by comparison of the two images of the artificial landmarks. The results in the experiments show both systems have successfully shown high performance, high accuracy and high implementation potential.
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